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Along with the blossom of open source projects comes the convenience for software plagiarism. A company, if less self-disciplined, may be tempted to plagiarize some open source projects for its own products. Although current plagiarism detection tools appear sufficient for academic use, they are nevertheless short for fighting against serious plagiarists.(More)
Top-<i>k</i> query asks for <i>k</i> tuples ordered according to a specific ranking function that combines the values from multiple participating attributes. The combined score function is usually linear. To efficiently answer top-<i>k</i> queries, preprocessing and indexing the data have been used to speed up the run time performance. Many indexing methods(More)
Given a set of model graphs D and a query graph q, containment search aims to find all model graphs g ∈ D such that q contains g (q ⊇ g). Due to the wide adoption of graph models, fast containment search of graph data finds many applications in various domains. In comparison to traditional graph search that retrieves all the graphs containing q (q ⊆ g),(More)
OLAP (On-Line Analytical Processing) is an important notion in data analysis. Recently, more and more graph or networked data sources come into being. There exists a similar need to deploy graph analysis from different perspectives and with multiple granularities. However, traditional OLAP technology cannot handle such demands because it does not consider(More)
Recently, there arise a large number of graphs with massive sizes and complex structures in many new applications, such as biological networks, social networks, and the Web, demanding powerful data mining methods. Due to inherent noise or data diversity, it is crucial to address the issue of approximation, if one wants to mine patterns that are potentially(More)
Graphs are prevalent in many domains such as Bioinformatics, social networks, Web and cyber-security. Graph pattern mining has become an important tool in the management and analysis of complexly structured data, where example applications include indexing, clustering and classification. Existing graph mining algorithms have achieved great success by(More)
Databases and data warehouse systems have been evolving from handling normalized spreadsheets stored in relational databases, to managing and analyzing diverse application-oriented data with complex interconnecting structures. Responding to this emerging trend, graphs have been growing rapidly and showing their critical importance in many applications, such(More)
In the past, quite a few fast algorithms have been developed to mine frequent patterns over graph data, with the large spectrum covering many variants of the problem. However, the real bottleneck for knowledge discovery on graphs is neither efficiency nor scalability, but the usability of patterns that are mined out. Currently, what the state-of-art(More)
Despite the recent emergence of many large-scale networks in different application domains, an important measure that captures a participant’s diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept.(More)